Control of cellular function depends on bipartite networks, in which one class of nodes (the controller) acts on the other class (the target) to regulate its function. Examples of cellular control networks include transcription factors, microRNAs, and protein kinases (1). In these networks, the control layer interacts with the target layer in a combinatorial, “many-to-many” fashion (see FIG. 1). In other words, each controller has many targets, the targets themselves are under the influence of many controlling molecules, and the target sets of different controllers overlap. Moreover, the number of controllers is usually significantly lower than the number of targets. While this is well recognized in biological systems, and the control problem in natural systems has been studied using simple models, for example, using Boolean networks, design principles of biological control are still not well understood. This lack of understanding also limits our ability to design more natural modes of multi-agent biological intervention. The network principles of combinatorial control in biology may serve as a guide to more effective combinatorial targeting patterns in combinatorial therapies, differentiation factors, and other modes of artificial control of biological systems.
Examples of Many-to-Many Control in Biology
A many-to-many combinatorial structure is not limited to the control of cells and it is found in all types of complex control in biology, the most striking example being the control of the organism by the nervous system, where connections among neurons have a many-to-many arrangement. The control of effectors by neurons has a simpler structure, as shown by motor units, where each motor neuron controls a distinct set of muscle fibers and the target sets are not overlapping, in a one-to-many fashion. The complexity of control structure might depend on the complexity of the target system.
Diseases such as cancer may also adapt by developing combinatorial strategies to counter intrinsic defense mechanisms and homeostatic reactions or extrinsic therapeutic interventions. An increasing body of evidence shows that the resistance of cancer to therapies involves molecules acting at multiple levels with many-to-many actions. This provides further support for the use of biomimetic therapeutic strategies of matching complexity.
Although combinatorial strategies to treat medical conditions have been proposed, there remains a need to effectively identify suitable therapeutic combinations of control molecules to modulate a plurality of endogenous molecules thereby producing desired biological effects.
Combinatorial Therapies
Recent development in the field of combinatorial therapies have been reviewed in (2). It is becoming increasingly evident to the clinician treating a complex disease or to the scientist studying a complex biological network that accurate control is more likely to be achieved by using multiple interventions. Since therapeutic molecules are increasing in specificity (as in the case of targeted drugs), and since our knowledge of the complexity of biological networks is advancing, it is becoming more feasible to consider drugs not as remedies for specific disorders but rather as a kit of molecular tools that can be combined for specific therapeutic purposes.
Because drug effects are dose-dependent, several doses need to be studied and, when therapeutic interventions on multiple targets are necessary, the number of possible combinations rises very quickly (this problem is often referred to as combinatorial explosion). For example, if we were to study all combinations of 6 out of 100 compounds (including partial combinations containing only some of these compounds) at 3 different doses we would have S6j=1 Binomial(100,j)*3j=8.9*1011 possibilities. This example suggests that the problem will require a qualitatively new approach rather than more efficient screening technology alone. Many cancer chemotherapy regimens are composed of 6 or more drugs from a pool of more than 100 clinically used anticancer compounds.
The traditional approach to combination therapy has been called empirical (3, 4) rather than systematic. A common assumption in the empirical approach is that only drugs that are effective individually should be used as part of a drug combination (5).
The limitations of this traditional approach to drug combinations have been described in a Commentary in The New England Journal of Medicine (6). Other reviews stressing the need for a more systematic approach to combination therapy have been published by Dancey and Chen (7), Hopkins (8) and Zimmerman et. al. (9). An editorial (3), commenting on the disappointing results of a clinical trial of combination therapy for colorectal cancer (10), suggested that combining the new targeted therapies might be even more challenging than combining cytotoxic chemotherapies, because of subtle interactions in intracellular signaling.